
#' 1. There are actually many (infinite) combinations that can produce the same
#.    expected utility for both actions: but the posterior probabilities will always
#     have to balance out the differences in the utility function. So, what is
#     important is that for a given utility function, there will be some 'point
#     of indifference'

#' 2. What matters is the relative information: if the prior is close to 50/50,
#     then the likelihood has more infuence, if the likelihood is 50/50 given a
#     measurement (the measurement is uninformative), the prior is more important.
#     But the critical insite from Bayes Rule and the Bayesian approach is that what
#     matters is the relative information you gain from a measurement, and that
#     you can use all of this information for your decision.

#' 3. The model gives us a very precise way to think about how we *should* combine
#     information and how we *should* act, GIVEN some assumption about our goals.
#     In this case, if we assume we are trying to maximize expected utility--we can
#     state what an animal or subject should do.

#' 4. There are lots of possible extensions. Humans may not always try to maximize
#     utility; humans and animals might not be able to calculate or represent probabiltiy
#     distributions exactly; The utility function might be more complicated; etc.